Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.7 KiB
Average record size in memory274.8 B

Variable types

Text1
Numeric10
Categorical6

Alerts

ACL_Risk_Score is highly overall correlated with Fatigue_Score and 2 other fieldsHigh correlation
Fatigue_Score is highly overall correlated with ACL_Risk_ScoreHigh correlation
Injury_Indicator is highly overall correlated with ACL_Risk_Score and 1 other fieldsHigh correlation
Load_Balance_Score is highly overall correlated with ACL_Risk_Score and 2 other fieldsHigh correlation
Training_Hours_Per_Week is highly overall correlated with Load_Balance_ScoreHigh correlation
Injury_Indicator is highly imbalanced (63.4%) Imbalance
Athlete_ID has unique values Unique

Reproduction

Analysis started2025-08-31 19:19:50.478128
Analysis finished2025-08-31 19:20:02.733897
Duration12.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Athlete_ID
Text

Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
2025-08-31T19:20:03.095532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st rowA001
2nd rowA002
3rd rowA003
4th rowA004
5th rowA005
ValueCountFrequency (%)
a001 1
 
0.5%
a002 1
 
0.5%
a003 1
 
0.5%
a004 1
 
0.5%
a005 1
 
0.5%
a006 1
 
0.5%
a007 1
 
0.5%
a008 1
 
0.5%
a009 1
 
0.5%
a010 1
 
0.5%
Other values (190) 190
95.0%
2025-08-31T19:20:03.590813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 200
25.0%
1 140
17.5%
0 139
17.4%
2 41
 
5.1%
3 40
 
5.0%
4 40
 
5.0%
5 40
 
5.0%
6 40
 
5.0%
7 40
 
5.0%
8 40
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 200
25.0%
1 140
17.5%
0 139
17.4%
2 41
 
5.1%
3 40
 
5.0%
4 40
 
5.0%
5 40
 
5.0%
6 40
 
5.0%
7 40
 
5.0%
8 40
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 200
25.0%
1 140
17.5%
0 139
17.4%
2 41
 
5.1%
3 40
 
5.0%
4 40
 
5.0%
5 40
 
5.0%
6 40
 
5.0%
7 40
 
5.0%
8 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 200
25.0%
1 140
17.5%
0 139
17.4%
2 41
 
5.1%
3 40
 
5.0%
4 40
 
5.0%
5 40
 
5.0%
6 40
 
5.0%
7 40
 
5.0%
8 40
 
5.0%

Age
Real number (ℝ)

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.17
Minimum18
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:03.689139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q119
median21
Q323
95-th percentile24
Maximum24
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.002787
Coefficient of variation (CV)0.09460496
Kurtosis-1.2058754
Mean21.17
Median Absolute Deviation (MAD)2
Skewness-0.054385542
Sum4234
Variance4.0111558
MonotonicityNot monotonic
2025-08-31T19:20:03.773962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
24 37
18.5%
21 34
17.0%
22 29
14.5%
19 27
13.5%
20 26
13.0%
18 24
12.0%
23 23
11.5%
ValueCountFrequency (%)
18 24
12.0%
19 27
13.5%
20 26
13.0%
21 34
17.0%
22 29
14.5%
23 23
11.5%
24 37
18.5%
ValueCountFrequency (%)
24 37
18.5%
23 23
11.5%
22 29
14.5%
21 34
17.0%
20 26
13.0%
19 27
13.5%
18 24
12.0%

Gender
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
Female
107 
Male
93 

Length

Max length6
Median length6
Mean length5.07
Min length4

Characters and Unicode

Total characters1014
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 107
53.5%
Male 93
46.5%

Length

2025-08-31T19:20:03.885294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:03.957978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 107
53.5%
male 93
46.5%

Most occurring characters

ValueCountFrequency (%)
e 307
30.3%
a 200
19.7%
l 200
19.7%
F 107
 
10.6%
m 107
 
10.6%
M 93
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 307
30.3%
a 200
19.7%
l 200
19.7%
F 107
 
10.6%
m 107
 
10.6%
M 93
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 307
30.3%
a 200
19.7%
l 200
19.7%
F 107
 
10.6%
m 107
 
10.6%
M 93
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 307
30.3%
a 200
19.7%
l 200
19.7%
F 107
 
10.6%
m 107
 
10.6%
M 93
 
9.2%

Height_cm
Real number (ℝ)

Distinct39
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.805
Minimum160
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:04.046276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile161
Q1171
median182.5
Q3191
95-th percentile197
Maximum199
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.529598
Coefficient of variation (CV)0.063768138
Kurtosis-1.1241145
Mean180.805
Median Absolute Deviation (MAD)9.5
Skewness-0.30494182
Sum36161
Variance132.93163
MonotonicityNot monotonic
2025-08-31T19:20:04.173040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
192 13
 
6.5%
185 10
 
5.0%
171 8
 
4.0%
181 8
 
4.0%
189 7
 
3.5%
188 7
 
3.5%
194 7
 
3.5%
191 7
 
3.5%
175 7
 
3.5%
182 7
 
3.5%
Other values (29) 119
59.5%
ValueCountFrequency (%)
160 6
3.0%
161 5
2.5%
162 5
2.5%
163 5
2.5%
164 7
3.5%
165 3
1.5%
166 3
1.5%
167 5
2.5%
168 2
 
1.0%
169 4
2.0%
ValueCountFrequency (%)
199 5
 
2.5%
198 4
 
2.0%
197 2
 
1.0%
196 6
3.0%
195 4
 
2.0%
194 7
3.5%
193 5
 
2.5%
192 13
6.5%
191 7
3.5%
190 4
 
2.0%

Weight_kg
Real number (ℝ)

Distinct45
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.475
Minimum55
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:04.306162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile57.95
Q167
median77.5
Q389
95-th percentile98
Maximum99
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.440789
Coefficient of variation (CV)0.16057811
Kurtosis-1.1108918
Mean77.475
Median Absolute Deviation (MAD)11.5
Skewness-0.0070655216
Sum15495
Variance154.77324
MonotonicityNot monotonic
2025-08-31T19:20:04.425716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
90 9
 
4.5%
82 9
 
4.5%
99 8
 
4.0%
89 8
 
4.0%
79 8
 
4.0%
67 8
 
4.0%
73 7
 
3.5%
66 7
 
3.5%
95 6
 
3.0%
93 6
 
3.0%
Other values (35) 124
62.0%
ValueCountFrequency (%)
55 5
2.5%
56 2
 
1.0%
57 3
1.5%
58 1
 
0.5%
59 5
2.5%
60 3
1.5%
61 3
1.5%
62 5
2.5%
63 5
2.5%
64 5
2.5%
ValueCountFrequency (%)
99 8
4.0%
98 3
 
1.5%
97 1
 
0.5%
96 2
 
1.0%
95 6
3.0%
94 1
 
0.5%
93 6
3.0%
92 4
2.0%
91 5
2.5%
90 9
4.5%

Position
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Forward
70 
Center
66 
Guard
64 

Length

Max length7
Median length6
Mean length6.03
Min length5

Characters and Unicode

Total characters1206
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCenter
2nd rowForward
3rd rowGuard
4th rowGuard
5th rowCenter

Common Values

ValueCountFrequency (%)
Forward 70
35.0%
Center 66
33.0%
Guard 64
32.0%

Length

2025-08-31T19:20:04.566870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:04.647351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
forward 70
35.0%
center 66
33.0%
guard 64
32.0%

Most occurring characters

ValueCountFrequency (%)
r 270
22.4%
a 134
11.1%
d 134
11.1%
e 132
10.9%
w 70
 
5.8%
o 70
 
5.8%
F 70
 
5.8%
C 66
 
5.5%
n 66
 
5.5%
t 66
 
5.5%
Other values (2) 128
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1206
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 270
22.4%
a 134
11.1%
d 134
11.1%
e 132
10.9%
w 70
 
5.8%
o 70
 
5.8%
F 70
 
5.8%
C 66
 
5.5%
n 66
 
5.5%
t 66
 
5.5%
Other values (2) 128
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1206
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 270
22.4%
a 134
11.1%
d 134
11.1%
e 132
10.9%
w 70
 
5.8%
o 70
 
5.8%
F 70
 
5.8%
C 66
 
5.5%
n 66
 
5.5%
t 66
 
5.5%
Other values (2) 128
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1206
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 270
22.4%
a 134
11.1%
d 134
11.1%
e 132
10.9%
w 70
 
5.8%
o 70
 
5.8%
F 70
 
5.8%
C 66
 
5.5%
n 66
 
5.5%
t 66
 
5.5%
Other values (2) 128
10.6%

Training_Intensity
Real number (ℝ)

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.105
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:04.719253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.49904
Coefficient of variation (CV)0.48952792
Kurtosis-1.1077792
Mean5.105
Median Absolute Deviation (MAD)2
Skewness-0.1147129
Sum1021
Variance6.245201
MonotonicityNot monotonic
2025-08-31T19:20:04.809475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 31
15.5%
6 28
14.0%
5 26
13.0%
4 24
12.0%
1 23
11.5%
3 21
10.5%
9 17
8.5%
7 16
8.0%
2 14
7.0%
ValueCountFrequency (%)
1 23
11.5%
2 14
7.0%
3 21
10.5%
4 24
12.0%
5 26
13.0%
6 28
14.0%
7 16
8.0%
8 31
15.5%
9 17
8.5%
ValueCountFrequency (%)
9 17
8.5%
8 31
15.5%
7 16
8.0%
6 28
14.0%
5 26
13.0%
4 24
12.0%
3 21
10.5%
2 14
7.0%
1 23
11.5%

Training_Hours_Per_Week
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.315
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:04.898567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q17
median11
Q315
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4389522
Coefficient of variation (CV)0.39230687
Kurtosis-1.2137635
Mean11.315
Median Absolute Deviation (MAD)4
Skewness0.1876336
Sum2263
Variance19.704296
MonotonicityNot monotonic
2025-08-31T19:20:04.991707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 20
 
10.0%
11 19
 
9.5%
7 18
 
9.0%
6 17
 
8.5%
12 14
 
7.0%
18 13
 
6.5%
15 12
 
6.0%
8 12
 
6.0%
10 12
 
6.0%
19 11
 
5.5%
Other values (5) 52
26.0%
ValueCountFrequency (%)
5 20
10.0%
6 17
8.5%
7 18
9.0%
8 12
6.0%
9 11
5.5%
10 12
6.0%
11 19
9.5%
12 14
7.0%
13 9
4.5%
14 10
5.0%
ValueCountFrequency (%)
19 11
5.5%
18 13
6.5%
17 11
5.5%
16 11
5.5%
15 12
6.0%
14 10
5.0%
13 9
4.5%
12 14
7.0%
11 19
9.5%
10 12
6.0%
Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2
69 
1
67 
3
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%

Length

2025-08-31T19:20:05.151136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:05.255521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%

Most occurring characters

ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 69
34.5%
1 67
33.5%
3 64
32.0%
Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
1
62 
4
47 
2
46 
3
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%

Length

2025-08-31T19:20:05.375189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:05.479436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%

Most occurring characters

ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 62
31.0%
4 47
23.5%
2 46
23.0%
3 45
22.5%
Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
1
69 
2
67 
3
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Length

2025-08-31T19:20:05.631354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:05.723772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Most occurring characters

ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 69
34.5%
2 67
33.5%
3 64
32.0%

Fatigue_Score
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.92
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:05.823543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5605433
Coefficient of variation (CV)0.52043563
Kurtosis-1.2395187
Mean4.92
Median Absolute Deviation (MAD)2
Skewness0.0191194
Sum984
Variance6.5563819
MonotonicityNot monotonic
2025-08-31T19:20:05.947326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 28
14.0%
7 24
12.0%
5 23
11.5%
4 22
11.0%
6 22
11.0%
8 22
11.0%
1 21
10.5%
3 19
9.5%
9 19
9.5%
ValueCountFrequency (%)
1 21
10.5%
2 28
14.0%
3 19
9.5%
4 22
11.0%
5 23
11.5%
6 22
11.0%
7 24
12.0%
8 22
11.0%
9 19
9.5%
ValueCountFrequency (%)
9 19
9.5%
8 22
11.0%
7 24
12.0%
6 22
11.0%
5 23
11.5%
4 22
11.0%
3 19
9.5%
2 28
14.0%
1 21
10.5%

Performance_Score
Real number (ℝ)

Distinct49
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.465
Minimum50
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:06.546471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile53
Q162
median74
Q386.25
95-th percentile97.05
Maximum99
Range49
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation14.636939
Coefficient of variation (CV)0.19656132
Kurtosis-1.2367314
Mean74.465
Median Absolute Deviation (MAD)12
Skewness0.10293639
Sum14893
Variance214.23997
MonotonicityNot monotonic
2025-08-31T19:20:06.776412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
64 8
 
4.0%
53 7
 
3.5%
74 7
 
3.5%
70 7
 
3.5%
62 7
 
3.5%
68 6
 
3.0%
56 6
 
3.0%
60 6
 
3.0%
97 6
 
3.0%
85 6
 
3.0%
Other values (39) 134
67.0%
ValueCountFrequency (%)
50 3
1.5%
51 3
1.5%
52 1
 
0.5%
53 7
3.5%
54 2
 
1.0%
55 4
2.0%
56 6
3.0%
57 4
2.0%
58 5
2.5%
59 6
3.0%
ValueCountFrequency (%)
99 4
2.0%
98 6
3.0%
97 6
3.0%
96 4
2.0%
95 5
2.5%
94 3
1.5%
93 6
3.0%
92 3
1.5%
91 5
2.5%
90 3
1.5%

Team_Contribution_Score
Real number (ℝ)

Distinct49
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.63
Minimum50
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:06.974189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q160.75
median72
Q385
95-th percentile95
Maximum99
Range49
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation14.432762
Coefficient of variation (CV)0.19871626
Kurtosis-1.2545505
Mean72.63
Median Absolute Deviation (MAD)12.5
Skewness0.052555975
Sum14526
Variance208.30462
MonotonicityNot monotonic
2025-08-31T19:20:07.197423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
51 9
 
4.5%
52 8
 
4.0%
84 7
 
3.5%
65 7
 
3.5%
57 7
 
3.5%
91 6
 
3.0%
63 6
 
3.0%
67 6
 
3.0%
70 6
 
3.0%
55 6
 
3.0%
Other values (39) 132
66.0%
ValueCountFrequency (%)
50 3
 
1.5%
51 9
4.5%
52 8
4.0%
53 5
2.5%
54 3
 
1.5%
55 6
3.0%
56 2
 
1.0%
57 7
3.5%
58 4
2.0%
59 1
 
0.5%
ValueCountFrequency (%)
99 2
 
1.0%
98 3
1.5%
96 2
 
1.0%
95 4
2.0%
94 5
2.5%
93 5
2.5%
92 1
 
0.5%
91 6
3.0%
90 4
2.0%
89 3
1.5%

Load_Balance_Score
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.395
Minimum62
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:07.390805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile76.9
Q189
median98
Q3100
95-th percentile100
Maximum100
Range38
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.6604847
Coefficient of variation (CV)0.09272964
Kurtosis1.1397292
Mean93.395
Median Absolute Deviation (MAD)2
Skewness-1.3561907
Sum18679
Variance75.003995
MonotonicityNot monotonic
2025-08-31T19:20:07.570330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
100 89
44.5%
98 9
 
4.5%
91 9
 
4.5%
94 7
 
3.5%
93 7
 
3.5%
95 5
 
2.5%
84 5
 
2.5%
88 5
 
2.5%
89 5
 
2.5%
78 5
 
2.5%
Other values (21) 54
27.0%
ValueCountFrequency (%)
62 1
 
0.5%
66 1
 
0.5%
69 2
 
1.0%
70 2
 
1.0%
71 1
 
0.5%
72 1
 
0.5%
74 1
 
0.5%
75 1
 
0.5%
77 1
 
0.5%
78 5
2.5%
ValueCountFrequency (%)
100 89
44.5%
99 5
 
2.5%
98 9
 
4.5%
97 5
 
2.5%
96 4
 
2.0%
95 5
 
2.5%
94 7
 
3.5%
93 7
 
3.5%
92 3
 
1.5%
91 9
 
4.5%

ACL_Risk_Score
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.47
Minimum2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-08-31T19:20:07.694021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile17
Q133
median45
Q360
95-th percentile77.1
Maximum100
Range98
Interquartile range (IQR)27

Descriptive statistics

Standard deviation18.943956
Coefficient of variation (CV)0.40765991
Kurtosis-0.2171803
Mean46.47
Median Absolute Deviation (MAD)13
Skewness0.20662931
Sum9294
Variance358.87347
MonotonicityNot monotonic
2025-08-31T19:20:07.848556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 8
 
4.0%
47 7
 
3.5%
33 6
 
3.0%
35 6
 
3.0%
61 6
 
3.0%
50 5
 
2.5%
49 5
 
2.5%
28 5
 
2.5%
44 5
 
2.5%
41 5
 
2.5%
Other values (63) 142
71.0%
ValueCountFrequency (%)
2 1
0.5%
4 1
0.5%
7 1
0.5%
9 2
1.0%
12 2
1.0%
13 1
0.5%
16 1
0.5%
17 2
1.0%
18 2
1.0%
19 1
0.5%
ValueCountFrequency (%)
100 1
 
0.5%
94 1
 
0.5%
93 1
 
0.5%
91 1
 
0.5%
84 1
 
0.5%
81 3
1.5%
79 2
1.0%
77 2
1.0%
76 2
1.0%
75 1
 
0.5%

Injury_Indicator
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
0
186 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Length

2025-08-31T19:20:07.967316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-31T19:20:08.029155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 186
93.0%
1 14
 
7.0%

Interactions

2025-08-31T19:20:01.472900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.233505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.169743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:53.808582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.099035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.109757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.335237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.317959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.231846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.185700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.562298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.322931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.269232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:53.934936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.211368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.220632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.427420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.411100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.338520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.281821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.656735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.418579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.369509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.081429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.315886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.315756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.533420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.504005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.432852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.395889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.744516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.504626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.516745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.208457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.407086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.404710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.629303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.586529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.517934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.486393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.840052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.599404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.672741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.345017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.509126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.504579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.725325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.680222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.617230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.882849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.932837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.690373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.818684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.477408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.608698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.606693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.822317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.775911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.719034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.980350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:02.030449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.788807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.964468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.627399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.706508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.701901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.918688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.872951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.821313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.077784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:02.118459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.881301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:53.103869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.758609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.808368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.041584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.010437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.958259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.911155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.174543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:02.204517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:51.985580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:53.529351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:54.896528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.908971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.134081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.105114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.046282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.995217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.268039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:02.299489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:52.081060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:53.668908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:55.017086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:56.012016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:57.250594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:58.209268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:19:59.142005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:00.092604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-31T19:20:01.377557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-31T19:20:08.103219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACL_Risk_ScoreAgeFatigue_ScoreGenderHeight_cmInjury_IndicatorLoad_Balance_ScoreMatch_Count_Per_WeekPerformance_ScorePositionRecovery_Days_Per_WeekRest_Between_Events_DaysTeam_Contribution_ScoreTraining_Hours_Per_WeekTraining_IntensityWeight_kg
ACL_Risk_Score1.000-0.0220.6570.000-0.0590.793-0.5130.000-0.0300.0000.2430.0960.1060.1950.358-0.038
Age-0.0221.0000.0640.049-0.0150.0840.0800.0000.0190.0000.1710.000-0.058-0.0780.054-0.030
Fatigue_Score0.6570.0641.0000.085-0.0230.278-0.0820.000-0.0520.0830.0000.0000.0740.012-0.008-0.046
Gender0.0000.0490.0851.0000.0000.0000.0210.0000.0850.0560.0000.1060.0860.0000.1660.157
Height_cm-0.059-0.015-0.0230.0001.0000.0670.0180.000-0.0030.1210.0000.154-0.0630.044-0.0530.035
Injury_Indicator0.7930.0840.2780.0000.0671.0000.5310.0000.0000.0000.2870.0000.0700.0000.0000.099
Load_Balance_Score-0.5130.080-0.0820.0210.0180.5311.0000.071-0.0670.0430.3480.000-0.033-0.551-0.0870.040
Match_Count_Per_Week0.0000.0000.0000.0000.0000.0000.0711.0000.1690.0000.0000.0000.0000.0000.0000.086
Performance_Score-0.0300.019-0.0520.085-0.0030.000-0.0670.1691.0000.0000.0400.000-0.0260.073-0.124-0.086
Position0.0000.0000.0830.0560.1210.0000.0430.0000.0001.0000.0000.0940.0000.1790.0560.157
Recovery_Days_Per_Week0.2430.1710.0000.0000.0000.2870.3480.0000.0400.0001.0000.0000.0000.0000.0740.153
Rest_Between_Events_Days0.0960.0000.0000.1060.1540.0000.0000.0000.0000.0940.0001.0000.0400.1380.0720.000
Team_Contribution_Score0.106-0.0580.0740.086-0.0630.070-0.0330.000-0.0260.0000.0000.0401.000-0.0110.052-0.064
Training_Hours_Per_Week0.195-0.0780.0120.0000.0440.000-0.5510.0000.0730.1790.0000.138-0.0111.000-0.025-0.129
Training_Intensity0.3580.054-0.0080.166-0.0530.000-0.0870.000-0.1240.0560.0740.0720.052-0.0251.000-0.042
Weight_kg-0.038-0.030-0.0460.1570.0350.0990.0400.086-0.0860.1570.1530.000-0.064-0.129-0.0421.000

Missing values

2025-08-31T19:20:02.465489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-31T19:20:02.633983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Athlete_IDAgeGenderHeight_cmWeight_kgPositionTraining_IntensityTraining_Hours_Per_WeekRecovery_Days_Per_WeekMatch_Count_Per_WeekRest_Between_Events_DaysFatigue_ScorePerformance_ScoreTeam_Contribution_ScoreLoad_Balance_ScoreACL_Risk_ScoreInjury_Indicator
0A00124Female19599Center2132311995810040
1A00221Male19265Forward8141314556383730
2A00322Male16383Guard8821365862100620
3A00424Female19290Guard1131117827478510
4A00520Female17379Center391212905183490
5A00622Female18075Guard9143416748499540
6A00722Female17990Forward5131427975678841
7A00824Female16764Center6723326270100420
8A00919Female16691Guard4192332586780500
9A01020Female16263Center2833276252100350
Athlete_IDAgeGenderHeight_cmWeight_kgPositionTraining_IntensityTraining_Hours_Per_WeekRecovery_Days_Per_WeekMatch_Count_Per_WeekRest_Between_Events_DaysFatigue_ScorePerformance_ScoreTeam_Contribution_ScoreLoad_Balance_ScoreACL_Risk_ScoreInjury_Indicator
190A19121Female17679Guard7141128718374941
191A19222Female18576Guard9181229975379791
192A19324Female16781Forward1122414867494300
193A19420Female18867Center6152321646495400
194A19520Female18587Guard7182421845387420
195A19623Female16988Guard31134137794100390
196A19721Male18595Forward8534255661100560
197A19819Female19389Center5621158181100250
198A19919Male16655Center972319608197610
199A20022Male16375Forward5523125977100230